Publication Date

8-10-2017

Keywords

information technology, virtual data warehouse

Abstract

Background: In 2013, Kaiser Permanente Northern California (KPNC) developed a virtual data warehouse (VDW) query tool to enable clinicians and investigators to conduct rapid, online simple analyses of key VDW variables without requiring knowledge of SQL or other programming languages. However, the tool’s usability has been limited by the user interface: results were returned in a tabular format, which required the user to take time to study the results (or preprocess them) before making interpretations or gathering insights. The Kaiser Permanente Mid-Atlantic States (KPMAS) and KPNC teams collaborated to overcome this barrier to usability with the design of Population Insight Tool. Our goals were to: 1) Explore new ways to use technology to accelerate users’ abilities to query the VDW; and 2) Develop a user-friendly approach to enhance users’ abilities to sift through the results for insights.

Methods: Our main challenge was how to handle changes to the output views based on impromptu changes to the user queries. No “off-the-shelf” business intelligence tools (like Tableau®) could be used –– because they all required prior knowledge of user queries to prebuild output views –– so we had to design our own solution. Front-end web development framework was used to build a user-friendly query page with drag-and-drop and search-by-keywords features to build queries. The insight tool system architecture was set up to leverage KPNC query engine to query the KPMAS VDW Teradata database and return results. New programs were designed to process the result data and automatically generate a customized dashboard with interactive visuals.

Results: For the clinician/investigator query “Show patient cohort with diagnosis of multiple sclerosis,” the insight tool shows results stratified by demographics along with other user-selected query items. Users can directly interact with data to rapidly conduct more detailed analyses and gather insight about specific subgroups such as female, black, non-Hispanic, 21–35 age-group, etc.

Conclusion: The insight tool was designed to empower clinicians/investigators to query VDW data directly. It is built to give more flexibility for data analysis with faster access to results in an easy to understand format. It also offers features to drill down to more effectively help the user understand nuances in the data.